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Review Paper: Health Monitoring of Civil Infrastructure

Chang, Peter C., Flatau, Alison, Liu, S. C.·2003·Structural Health Monitoring

Abstract

Increased awareness of the economic and social effects of aging, deterioration and extreme events on civil infrastructure has been accompanied by recognition of the need for advanced structural health monitoring and damage detection tools. Today, these tasks are done by visual inspection and very traditional methods such as the tap test. This labor-intensive task is done at a frequency of less than once every two years for bridges, and on an as-needed basis for other infrastructures such as buildings. Structural health monitoring techniques based on changes in dynamic characteristics have been studied for the last three decades. When the damage is substantial, these methods have some success in determining if damage has occurred. At incipient stages of damage, however, the existing methods are not as successful. A number of new research projects have been funded to improve the damage detection methods including the use of innovative signal processing, new sensors, and control theory. This survey paper highlights these new research directions.

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Metadata

Authors
Chang, P. C., Flatau, A., & Liu, S. C.
Venue
Structural Health Monitoring
Published
2003-09
Volume
2
Issue
3
Pages
257-267
ISSN
  • 1475-9217 (ISSN-L)
  • 1741-3168
OpenAlex
W2169534075
Cited by
982
References
0
FWCI
28.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Review Paper: Health Monitoring of Civil Infrastructure. https://app.cochat.ai/papers/10.1177/1475921703036169
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Chang, P. C., Flatau, A., & Liu, S. C. (2003). Review Paper: Health Monitoring of Civil Infrastructure. Structural Health Monitoring. https://doi.org/10.1177/1475921703036169

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.